Language Translation with RNN

References

Guide: https://github.com/tommytracey/AIND-Capstone https://tommytracey.github.io/AIND-Capstone/machine_translation.html

Why TimeDistributedDenseLayer: https://datascience.stackexchange.com/questions/10836/the-difference-between-dense-and-timedistributeddense-of-keras

Keras Documentation: https://tensorflow.rstudio.com/reference/keras/

Stackoverflow: https://stackoverflow.com/questions/10961141/setting-up-a-3d-matrix-in-r-and-accessing-certain-elements

Attempt to train words using 8-10 Words accuracy could be due to PADDING

Importing of Libraries

library(keras)
library(tensorflow)
library(tokenizers)
library(dplyr)
library(png)
library(reticulate)
library(abind)
library(ramify)
library(stringr)
library(deepviz)

language <- "French"
language_code <- "fr"
file_name <- paste0("translation_", language_code, ".csv")
train <- read.csv(file_name, encoding="UTF-8", stringsAsFactors=FALSE)

Amending column names

colnames(train) <- c("English", language)

train

Tokenizer

tokenize <- function(x){
  tokenizer <- text_tokenizer(num_words = 1000000)
  fit_text_tokenizer(tokenizer, x)
  sequences <- texts_to_sequences(tokenizer, x)
  return(c(sequences, tokenizer))
}

Padding

pad <- function(x, length=NULL){
  return(pad_sequences(x, maxlen = length, padding = 'post'))
}

Example for Tokenisation & Padding

text_sentences = c('The quick brown fox jumps over the lazy dog .',
    'By Jove , my quick study of lexicography won a prize .',
    'This is a short sentence .')
token_index <- length(text_sentences) + 1
output <- tokenize(text_sentences)
text_tokenized <- output[1:length(text_sentences)]
# print(output)

# Finding out the integer allocation to each word
tk <- output[[token_index]]$word_index
# print(tk)
# print(length(tk))
# print(table(tk))

Seeing the input vs output for each tokenized sentences

for(i in 1:length(text_sentences)){
  print(paste0("Sequence in Text ", i, ":"))
  print(paste0("Input: ", text_sentences[i]))
  print(paste0("Output: ", list(text_tokenized[[i]])))
  cat("\n")
}

[1] "Sequence in Text 1:"
[1] "Input: The quick brown fox jumps over the lazy dog ."
[1] "Output: c(1, 2, 4, 5, 6, 7, 1, 8, 9)"

[1] "Sequence in Text 2:"
[1] "Input: By Jove , my quick study of lexicography won a prize ."
[1] "Output: c(10, 11, 12, 2, 13, 14, 15, 16, 3, 17)"

[1] "Sequence in Text 3:"
[1] "Input: This is a short sentence ."
[1] "Output: c(18, 19, 3, 20, 21)"

Padding each tokenized sentences

padded_text <- pad(text_tokenized)
for(i in 1:length(text_sentences)){
  print(paste0("Sequence in Text ", i, ":"))
  print(paste0("Input: ", text_sentences[i]))
  print(paste0("Output: ", list(text_tokenized[[i]])))
  print(paste0("Output (Padded): ", list(padded_text[i,])))
}

[1] "Sequence in Text 1:"
[1] "Input: The quick brown fox jumps over the lazy dog ."
[1] "Output: c(1, 2, 4, 5, 6, 7, 1, 8, 9)"
[1] "Output (Padded): c(1, 2, 4, 5, 6, 7, 1, 8, 9, 0)"
[1] "Sequence in Text 2:"
[1] "Input: By Jove , my quick study of lexicography won a prize ."
[1] "Output: c(10, 11, 12, 2, 13, 14, 15, 16, 3, 17)"
[1] "Output (Padded): c(10, 11, 12, 2, 13, 14, 15, 16, 3, 17)"
[1] "Sequence in Text 3:"
[1] "Input: This is a short sentence ."
[1] "Output: c(18, 19, 3, 20, 21)"
[1] "Output (Padded): c(18, 19, 3, 20, 21, 0, 0, 0, 0, 0)"

Preprocessing Component (Tidying up of characters and sentences)

Getting Compiled English Text (Testing)

# n <- nrow(subset_train)
n <- 5
word_list <- list(train[, 1])[[1]][1:n]
# word_list
new_output <- tokenize(word_list)
new_text_tokenized <- new_output[1:n]
new_padded_text <- pad(new_text_tokenized)

for(i in 1:n){
  # if(i %% 100 != 0) next
  print(paste0("Sequence in Text ", i, ":"))
  print(paste0("Input: ", word_list[i]))
  print(paste0("Output: ", list(new_text_tokenized[[i]])))
  print(paste0("Output (Padded): ", list(new_padded_text[i,])))
  # cat("\n")
}

[1] "Sequence in Text 1:"
[1] "Input: new jersey is sometimes quiet during autumn , and it is snowy in april ."
[1] "Output: c(15, 16, 1, 8, 9, 2, 17, 3, 4, 1, 18, 5, 19)"
[1] "Output (Padded): c(15, 16, 1, 8, 9, 2, 17, 3, 4, 1, 18, 5, 19, 0, 0)"
[1] "Sequence in Text 2:"
[1] "Input: the united states is usually chilly during july , and it is usually freezing in november ."
[1] "Output: c(6, 10, 11, 1, 7, 20, 2, 21, 3, 4, 1, 7, 22, 5, 23)"
[1] "Output (Padded): c(6, 10, 11, 1, 7, 20, 2, 21, 3, 4, 1, 7, 22, 5, 23)"
[1] "Sequence in Text 3:"
[1] "Input: california is usually quiet during march , and it is usually hot in june ."
[1] "Output: c(24, 1, 7, 9, 2, 25, 3, 4, 1, 7, 26, 5, 12)"
[1] "Output (Padded): c(24, 1, 7, 9, 2, 25, 3, 4, 1, 7, 26, 5, 12, 0, 0)"
[1] "Sequence in Text 4:"
[1] "Input: the united states is sometimes mild during june , and it is cold in september ."
[1] "Output: c(6, 10, 11, 1, 8, 27, 2, 12, 3, 4, 1, 28, 5, 29)"
[1] "Output (Padded): c(6, 10, 11, 1, 8, 27, 2, 12, 3, 4, 1, 28, 5, 29, 0)"
[1] "Sequence in Text 5:"
[1] "Input: your least liked fruit is the grape , but my least liked is the apple ."
[1] "Output: c(30, 13, 14, 31, 1, 6, 32, 33, 34, 13, 14, 1, 6, 35)"
[1] "Output (Padded): c(30, 13, 14, 31, 1, 6, 32, 33, 34, 13, 14, 1, 6, 35, 0)"

Getting Compiled Other Language Text (Testing)

# n <- nrow(subset_train)
n <- 5
word_list <- list(train[, 2])[[1]][1:n]
new_output <- tokenize(word_list)
new_text_tokenized <- new_output[1:n]
new_padded_text <- pad(new_text_tokenized)

for(i in 1:n){
  # if(i %% 100 != 0) next
  print(paste0("Sequence in Text ", i, ":"))
  print(paste0("Input: ", word_list[i]))
  print(paste0("Output: ", list(new_text_tokenized[[i]])))
  print(paste0("Output (Padded): ", list(new_padded_text[i,])))
}

[1] "Sequence in Text 1:"
[1] "Input: new jersey est parfois calme pendant l' automne , et il est neigeux en avril ."
[1] "Output: c(15, 16, 1, 6, 7, 17, 18, 19, 3, 4, 1, 20, 2, 21)"
[1] "Output (Padded): c(15, 16, 1, 6, 7, 17, 18, 19, 3, 4, 1, 20, 2, 21)"
[1] "Sequence in Text 2:"
[1] "Input: les états-unis est généralement froid en juillet , et il gèle habituellement en novembre ."
[1] "Output: c(8, 9, 10, 1, 5, 11, 2, 22, 3, 4, 23, 24, 2, 25)"
[1] "Output (Padded): c(8, 9, 10, 1, 5, 11, 2, 22, 3, 4, 23, 24, 2, 25)"
[1] "Sequence in Text 3:"
[1] "Input: california est généralement calme en mars , et il est généralement chaud en juin ."
[1] "Output: c(26, 1, 5, 7, 2, 27, 3, 4, 1, 5, 28, 2, 12)"
[1] "Output (Padded): c(26, 1, 5, 7, 2, 27, 3, 4, 1, 5, 28, 2, 12, 0)"
[1] "Sequence in Text 4:"
[1] "Input: les états-unis est parfois légère en juin , et il fait froid en septembre ."
[1] "Output: c(8, 9, 10, 1, 6, 29, 2, 12, 3, 4, 30, 11, 2, 31)"
[1] "Output (Padded): c(8, 9, 10, 1, 6, 29, 2, 12, 3, 4, 30, 11, 2, 31)"
[1] "Sequence in Text 5:"
[1] "Input: votre moins aimé fruit est le raisin , mais mon moins aimé est la pomme ."
[1] "Output: c(32, 13, 14, 33, 1, 34, 35, 36, 37, 13, 14, 1, 38, 39)"
[1] "Output (Padded): c(32, 13, 14, 33, 1, 34, 35, 36, 37, 13, 14, 1, 38, 39)"

Preprocessing both languages compilations

preprocess_text <- function(x, y){
  output_x <- tokenize(x)
  output_y <- tokenize(y)
  
  preprocess_x <- output_x[1:length(x)]; x_tk <- output_x[[length(x) + 1]]$word_index
  preprocess_y <- output_y[1:length(y)]; y_tk <- output_y[[length(y) + 1]]$word_index
  
  # print(preprocess_x)
  
  preprocess_x <- pad(preprocess_x)
  preprocess_y <- pad(preprocess_y)
  
  # print(preprocess_x)
  
  # Converting from a 2D matrix to a 3D tensor
  # preprocess_x <- array(preprocess_x[[1]], c(dim(preprocess_x[[1]])[1], dim(preprocess_x[[1]])[2], 1))
  # preprocess_y <- array(preprocess_y[[1]], c(dim(preprocess_y[[1]])[1], dim(preprocess_y[[1]])[2], 1))
  
  return(list(preprocess_x, preprocess_y, x_tk, y_tk))
}

Full Data

train_x <- list(train[, 1])[[1]]
train_y <- list(train[, 2])[[1]]
# print(subset_train_x)

process_output <- preprocess_text(train_x, train_y)
# print(process_output[4],)
preprocess_x <- process_output[1]; preprocess_y <- process_output[2]; x_tk <- process_output[3]; y_tk <- process_output[4]
# print(preprocess_x[[1]])
# print(preprocess_y[[1]])


# Conversion back to list of words from tokenized word list
# attributes(x_tk[[1]])$names
# length(y_tk[[1]])

Obtaining the maximum column number and re-padding

col_x <- dim(preprocess_x[[1]])[2]
col_y <- dim(preprocess_y[[1]])[2]

if(col_x >= col_y){
  max_col <- col_x
}else{
  max_col <- col_y
}

tmp_x <- pad(preprocess_x[[1]], max_col)
tmp_y <- pad(preprocess_y[[1]], max_col)

Calculating Sparsity (Extra)

calculate_sparsity <- function(df_matrix){
  zero_count <- 0
  total_count <- nrow(df_matrix) * ncol(tmp_x)
  for(i in 1:nrow(df_matrix)){
    for(j in 1:ncol(df_matrix)){
      if(df_matrix[i, j] == 0){
        zero_count = zero_count + 1
      }
    }
  }
  zero_count/total_count
}

print(paste("The Sparsity of the matrix is: ", round(calculate_sparsity(tmp_x)*100, 2), "%"))

[1] "The Sparsity of the matrix is:  46.37 %"

Conversion of 2D matrix to tensor

convert2tensor <- function(preprocess_data){
  preprocess_data <- array(preprocess_data, c(dim(preprocess_data)[1], dim(preprocess_data)[2], 1))
  return(preprocess_data)
}

# array(preprocess_x[[1]], c(dim(preprocess_x[[1]])[1], dim(preprocess_x[[1]])[2], 1))
# dim(array(preprocess_x[[1]], c(dim(preprocess_x[[1]])[1], dim(preprocess_x[[1]])[2], 1)))[2:3]

Converting to tensor

tensor_x <- convert2tensor(tmp_x)
dim(tensor_x)

[1] 137860     21      1

tensor_x[1, , ]

 [1] 17 23  1  8 67  4 39  7  3  1 55  2 44  0  0  0  0  0  0  0  0

tensor_y <- convert2tensor(tmp_y)
# tensor_y

Converting the logits back to text

logits_to_text <- function(logits, tokenizer, predict=FALSE){
  tokenizer_words <- attributes(tokenizer[[1]])$names
  text <- c()
  if(predict == TRUE){
    logits <- logits - 1 ## For prediction conversion only 
  }
  for(i in logits){
    if(i == 0){
      text <- c(text, "<PAD>")
    }else{
      text <- c(text, tokenizer_words[i])
    }
  }
  return(text)
}

# Testing to convert the first row back to text
# preprocess_x[[1]][1, ]
# preprocess_x[[1]]
logits_to_text(preprocess_x[[1]][1, ], x_tk)

 [1] "new"       "jersey"    "is"        "sometimes" "quiet"     "during"    "autumn"    "and"       "it"        "is"        "snowy"    
[12] "in"        "april"     "<PAD>"     "<PAD>"    

Building a simple RNN model

# dim(tensor_y)
model_RNN <-  keras_model_sequential()
model_RNN %>% 
  layer_simple_rnn(units = 256, input_shape = dim(tensor_x)[2:3], return_sequences = TRUE) %>%
  layer_dense(units = 1024, activation = 'relu')%>%
  layer_dropout(rate = 0.5) %>%
  layer_dense(units = length(y_tk[[1]]) + 1, activation = 'softmax')

model_RNN %>% summary()

Model: "sequential_3"
_____________________________________________________________________________________________________________________________________________
 Layer (type)                                                  Output Shape                                            Param #               
=============================================================================================================================================
 simple_rnn_2 (SimpleRNN)                                      (None, 21, 256)                                         66048                 
                                                                                                                                             
 dense_7 (Dense)                                               (None, 21, 1024)                                        263168                
                                                                                                                                             
 dropout_3 (Dropout)                                           (None, 21, 1024)                                        0                     
                                                                                                                                             
 dense_6 (Dense)                                               (None, 21, 345)                                         353625                
                                                                                                                                             
=============================================================================================================================================
Total params: 682,841
Trainable params: 682,841
Non-trainable params: 0
_____________________________________________________________________________________________________________________________________________

model_RNN %>% compile(
  loss      = 'sparse_categorical_crossentropy',
  # optimizer = optimizer_rmsprop(),
  optimizer = optimizer_adam(learning_rate = 0.005),
  metrics=c('accuracy')
)

plot_model(model_RNN)

history = model_RNN %>% fit(
  x = tensor_x, y = tensor_y,
  epochs           = 10,
  batch_size = 1024,
  validation_split = 0.2,
)

Epoch 1/10

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108/108 [==============================] - 63s 574ms/step - loss: 2.0487 - accuracy: 0.5311

108/108 [==============================] - 68s 628ms/step - loss: 2.0487 - accuracy: 0.5311 - val_loss: 1.4762 - val_accuracy: 0.6104
Epoch 2/10

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108/108 [==============================] - 64s 595ms/step - loss: 1.3902 - accuracy: 0.6158

108/108 [==============================] - 70s 645ms/step - loss: 1.3902 - accuracy: 0.6158 - val_loss: 1.2241 - val_accuracy: 0.6361
Epoch 3/10

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108/108 [==============================] - 65s 606ms/step - loss: 1.2259 - accuracy: 0.6421

108/108 [==============================] - 71s 657ms/step - loss: 1.2259 - accuracy: 0.6421 - val_loss: 1.0995 - val_accuracy: 0.6726
Epoch 4/10

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108/108 [==============================] - 65s 606ms/step - loss: 1.1387 - accuracy: 0.6566

108/108 [==============================] - 71s 657ms/step - loss: 1.1387 - accuracy: 0.6566 - val_loss: 1.0247 - val_accuracy: 0.6876
Epoch 5/10

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108/108 [==============================] - 66s 613ms/step - loss: 1.0761 - accuracy: 0.6668

108/108 [==============================] - 72s 667ms/step - loss: 1.0761 - accuracy: 0.6668 - val_loss: 0.9956 - val_accuracy: 0.6730
Epoch 6/10

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108/108 [==============================] - 70s 652ms/step - loss: 1.0330 - accuracy: 0.6735

108/108 [==============================] - 76s 705ms/step - loss: 1.0330 - accuracy: 0.6735 - val_loss: 0.9510 - val_accuracy: 0.6945
Epoch 7/10

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108/108 [==============================] - 68s 629ms/step - loss: 1.0001 - accuracy: 0.6782

108/108 [==============================] - 74s 682ms/step - loss: 1.0001 - accuracy: 0.6782 - val_loss: 0.9234 - val_accuracy: 0.7003
Epoch 8/10

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108/108 [==============================] - 69s 635ms/step - loss: 0.9735 - accuracy: 0.6820

108/108 [==============================] - 74s 688ms/step - loss: 0.9735 - accuracy: 0.6820 - val_loss: 0.9021 - val_accuracy: 0.7001
Epoch 9/10

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108/108 [==============================] - 75s 695ms/step - loss: 0.9436 - accuracy: 0.6894 - val_loss: 0.8517 - val_accuracy: 0.7095
Epoch 10/10

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108/108 [==============================] - 72s 670ms/step - loss: 0.9322 - accuracy: 0.6906 - val_loss: 0.8449 - val_accuracy: 0.7082

plot(history)

`geom_smooth()` using formula 'y ~ x'

Prediction

Obtaining the output for prediction (Testing)

predict_output <- model_RNN %>% predict(matrix(tensor_x[5, ,], nrow=1))
# predict_output


predict_output <- argmax(predict_output, FALSE)
# train_x[5]
train_y[5]

[1] "votre moins aimé fruit est le raisin , mais mon moins aimé est la pomme ."

logits_to_text(predict_output, y_tk, predict = TRUE)

 [1] "votre" "fruit" "est"   "moins" "aimé" "la"    "pomme" "mais"  "mon"   "moins" "aimé" "est"   "la"    "<PAD>" "<PAD>" "<PAD>" "<PAD>"
[18] "<PAD>" "<PAD>" "<PAD>" "<PAD>"

Predictions for Training set

pred_translation <- function(i){
  predict_output <- model_RNN %>% predict(matrix(tensor_x[i, ,], nrow=1))
  predict_output <- argmax(predict_output, FALSE)
  converted_text <- logits_to_text(predict_output, y_tk, predict = TRUE)
  converted_text[converted_text == "<PAD>"] <- ""
  converted_text <- trimws(paste(converted_text, collapse = " "))
  print(paste("Input sentence:", train_x[i]))
  print(paste("Intended Output Sentence:", train_y[i]))
  print(paste("Predicted Output Sentence:", converted_text))
}

## `i` represents the index within the training set.
pred_translation(5)

[1] "Input sentence: your least liked fruit is the grape , but my least liked is the apple ."
[1] "Intended Output Sentence: votre moins aimé fruit est le raisin , mais mon moins aimé est la pomme ."
[1] "Predicted Output Sentence: votre fruit est moins aimé la pomme mais mon moins aimé est la"